Compunnel Inc.

Python Engineer with Machine Learning Background

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Python Engineer with a Machine Learning background, offering a 12+ month contract in Smithfield, RI or Westlake, TX (Hybrid). Key skills include Python, ML, Generative AI, API development, and AWS services.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 16, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Dallas, TX
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🧠 - Skills detailed
#Data Science #EC2 #API (Application Programming Interface) #FastAPI #Data Pipeline #Cloud #Flask #Langchain #AWS (Amazon Web Services) #Python #RDS (Amazon Relational Database Service) #Snowflake #ML (Machine Learning) #AI (Artificial Intelligence) #SageMaker #Deep Learning
Role description
Please find the position details below: Job Title: Python Engineer with Machine Learning background Location: Smithfield RI or Westlake, TX (Hybrid – 2 weeks onsite, 2 weeks remote) Duration: 12+ Months Contract with possibility of extension or Conversion Interview: 2 rounds What is the Client Looking For? Core Technical Skills • Python (primary language) – especially for ML, data pipelines, and APIs. • ML & Deep Learning – understanding of model training, tuning, and statistical analysis. • Gen AI Expertise: • Experience with Large Language Models (LLMs). • Prompt Engineering – and how you use it to interact with LLMs (this must be clearly shown on your resume). • Tools like LangChain and LlamaIndex. • Experience with RAG and Fine-Tuning of models. • API Development – using FastAPI or Flask. • AWS Cloud Services – especially SageMaker, EC2, EKS, RDS, and Snowflake. What is the Project? The focus is on building and improving AI/ML solutions, particularly Generative AI (Gen AI) systems. The main goals of the project are: • Design and implement Gen AI solutions: This includes areas like Retrieval-Augmented Generation (RAG), Prompt Engineering, Fine-tuning of LLMs, and more. • Develop APIs and integrate ML models: Building production-ready applications using Python-based APIs (e.g., FastAPI, Flask). • Deploy and manage ML models in AWS: Using services like SageMaker, EC2, EKS, and working with data platforms such as Snowflake or RDS. • Collaborate with data scientists: To create data pipelines, conduct experiments, and improve model performance. • Apply ML and deep learning algorithms: For automating and enhancing business operations.